PhD Studentship - Arterial Haemodynamics to quantify Plaque-Risk using Geometric Archetypes

Deadline: All year round

How to apply:

UK only

This 3.5-year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year.

Computational haemodynamic modelling provides a powerful framework for linking blood flow dynamics with cardiovascular disease, using in silico approaches to systematically study flow environments associated with vascular health and pathology. Atherosclerotic plaque formation in arteries is strongly influenced by local blood flow patterns. Disturbed haemodynamics are associated with plaque initiation and progression and can contribute to arterial narrowing and an increased risk of adverse cardiovascular outcomes. These flow disturbances are closely linked to arterial geometry, particularly features such as curvature, branching, bifurcations, narrowing, and local dilations.

This PhD project aims to develop a ‘haemodynamic fingerprinting’ framework that systematically links arterial geometry → blood flow patterns → atherogenic risk, without relying solely on patient-specific models. The project will identify universal flow features that promote plaque-prone conditions and organise them into a reusable plaque–flow atlas.

Key objectives include to:

• Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric archetypes (e.g. curved vessels, bifurcations, side branches, stenoses, and local dilations).
• Run systematic computational fluid dynamics simulations under physiologically representative conditions to form a large virtual cohort of arterial flow cases and extract haemodynamic metrics and reduced-order descriptors.
• Use data-driven techniques of dimensionality reduction and classification (e.g. PCA, clustering) to organise flow behaviour into interpretable categories.
• Build a plaque-flow atlas to identify the plaque-risk associated with geometric features.
• Explore validation strategies using patient-specific arterial geometries, where imaging data are available, to test the transferability of atlas-based predictions.

This project moves beyond single patient-specific studies to a systematic, mechanistic understanding of how geometry drives atherogenic flow. It introduces the concept of haemodynamic fingerprints and geometric archetypes applicable across multiple arterial systems and will produce a reusable plaque–flow atlas that can inform future CFD studies, experimental work, and clinical interpretation.

The PhD candidate will be supported by an interdisciplinary supervisory and research team with expertise spanning engineering, clinical sciences, and cardiovascular research, ensuring strong methodological training alongside clinically informed interpretation. The candidate will benefit from being associated with, and receiving support from, the Modelling and Simulation Centre and the British Heart Foundation (BHF) Manchester Centre of Research Excellence. These centres provide access to a vibrant interdisciplinary research community, advanced computational resources, and regular scientific and clinical engagement. The research environment offers opportunities for collaboration with clinicians, experimentalists, and data scientists, and supports the dissemination of research through high-quality journal publications, conference presentations, and open research outputs.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Essential skills:

• Interest in fluid mechanics and biological flows
• Strong quantitative and programming skills.
• Experience in CFD software.
• Strong written and verbal communication skills.

To apply please contact the main supervisor, Dr Sampad Sengupta - sampad.sengupta@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

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